Dashboards are an important data analytics tool for understanding business metrics and managing your business performance. However, if your dashboard is not designed well, it can be difficult to use for analysis, and ineffective for decision-making. Dashboard design should be simple and accessible, and reflect your company's branding and identity. Ensuring your analytical dashboard is readable, usable and accurate is heavily reliant on following best practice dashboard design.

In the wake of the disruption caused by the world’s turbulence over the past few years, the telecommunications industry has come out reasonably unscathed. There remain challenges in workforce management, particularly in call centers, and order backlogs for fiber broadband and other physical infrastructure are being worked through. But digital transformation programs are accelerating, services innovation around 5G is continuing apace, and results to the stock market have been robust.

Data backup is the last line of defense when a cyberattack occurs, especially when the attack is ransomware. With robust data backup technologies and procedures, an organization can return to a point-in-time prior to the attack and return to operations relatively quickly. But as data volumes continue to explode, ransomware attacks are growing more sophisticated and beginning to target that precious backup data and administrator functions.

Apache HBase has long been the database of choice for business-critical applications across industries. This is primarily because HBase provides unmatched scale, performance, and fault-tolerance that few other databases can come close to. Think petabytes of data spread across trillions of rows, ready for consumption in real-time.

Technology is always evolving, and so is the way we use it to collect and analyze data. From humble spreadsheet beginnings to fully automated business monitoring and AI-powered analysis, the range of analytics tools on offer today is astounding to consider. The business intelligence dashboard is one such option. It has existed for decades as a tool for organizations to monitor and analyze operational data - and it's not quite dead yet.

Modern data pipelines have become more business-critical than ever. Every company today is a data company, looking to leverage data analytics as a competitive advantage. But the complexity of the modern data stack imposes some significant challenges that are hindering organizations from realizing their goals and realizing the value of data.

Cloud complexity is an inevitability. Regardless of where an organization may be on their cloud journey – on-prem, in the public cloud, or managing an expanding hybrid cloud – the reality is managing the enterprise isn’t getting any easier. Demand continues to rise for greater access to more data across the organization to do things like run analytics and machine learning and to automate more processes.

You want to enable analytics, data science, or applications with data so you can answer questions, predict outcomes, discover relationships, or grow your business. But to do any of that, data must be stored in a manner to support these outcomes. This may be a simple decision when supporting a small, well-known use case, but it quickly becomes complicated as you scale the data volume, variety, workloads, and use cases.

Software company Slack is on a mission to make work simpler, more pleasant, and more productive. Millions of users across more than 150 countries use Slack to collaborate with team members, connect other tools and services, and access information. Marketers at Slack rely on large amounts of data to build custom audiences, manage subscriber consent preferences, and measure campaign performance.

Measuring how marketing is helping to drive your business is critical. But often marketing’s impact is not widely understood and appreciated.